Embodiments of the present invention relate generally to methods and systems for improving performance of processing nodes in a fabric and more particularly to changing the way in which processing, memory, storage, network, and cloud computing, are managed to significantly improve the efficiency and performance of commodity hardware.
As the size and complexity of data and the processes performed thereon continually increases, computer hardware is challenged to meet these demands. Current commodity hardware and software solutions from established server, network and storage providers are unable to meet the demands of Cloud Computing and Big Data environments. This is due, at least in part, to the way in which processing, memory, and storage are managed by those systems. Specifically, processing is separated from memory which is turn is separated from storage in current systems and each of processing, memory, and storage is managed separately by software. Each server and other computing device (referred to herein as a node) is in turn separated from other nodes by a physical computer network, managed separately by software and in turn the separate processing, memory, and storage associated with each node are managed by software on that node.
FIG. 1 is a block diagram illustrating an example of the separation data storage, memory, and processing within prior art commodity servers and network components. This example illustrates a system 100 in which commodity servers 105 and 110 are communicatively coupled with each other via a physical network 115 and network software 155 as known in the art. Also as known in the art, the servers can each execute any number of one or more applications 120a, 120b, 120c of any variety. As known in the art, each application 120a, 120b, 120c executes on a processor (not shown) and memory (not shown) of the server 105 and 110 using data stored in physical storage 150. Each server 105 and 110 maintains a directory 125 mapping the location of the data used by the applications 120a, 120b, 120c. Additionally, each server implements for each executing application 120a, 120b, 120c a software stack which includes an application representation 130 of the data, a database representation 135, a file system representation 140, and a storage representation 145.
While effective, there are three reasons that such implementations on current commodity hardware and software solutions from established server, network and storage providers are unable to meet the increasing demands of Cloud Computing and Big Data environments. One reason for the shortcomings of these implementations is their complexity. The software stack must be in place and every application must manage the separation of storage, memory, and processing as well as applying parallel server resources. Each application must trade-off algorithm parallelism, data organization and data movement which is extremely challenging to get correct, let alone considerations of performance and economics. This tends to lead to implementation of more batch oriented solutions in the applications, rather than the integrated real-time solutions preferred by most businesses. Additionally, separation of storage, memory, and processing, in such implementations also creates significant inefficiency for each layer of the software stack to find, move, and access a block of data due to the required instruction execution and latencies of each layer of the software stack and between the layers. Furthermore, this inefficiency limits the economic scaling possible and limits the data-size for all but the most extremely parallel algorithms. The reason for the latter is that the efficiency with which servers (processors or threads) can interact limits the amount of parallelism due to Amdahl's law. Hence, there is a need for improved methods and systems for managing processing, memory, and storage to significantly improve the performance of processing nodes.